As an indispensable key component of power transmission lines, power fittings provide a guarantee for stable power transmission. Once the electric power fittings have defects, it will bring huge hidden dangers, causing damage to transmission facilities or even large-scale power failure, affecting people’ s production and life. The traditional power transmission line maintenance mainly depends on manual on-site maintenance, which is not only dangerous, but also difficult to detect. The continuous progress of AI recognition technology provides a better method for the defect recognition of electric power fittings. At present, the target recognition accuracy of Faster R-CNN is high, but it is relatively low for small target objects such as screws. Firstly, the features are extracted and marked by the double feature fusion operator, then input into the improved Faster R-CNN model with the introduction of mixed attention mechanism for feature re extraction. The features with high coincidence degree are fused, and the defects are classified and recognized, which can effectively identify the screws in the small power fittings. The experiment shows that the improved Faster R-CNN based on dual feature fusion in this paper has obvious improvement effect compared with the traditional Faster R-CNN and YOLO. The average accuracy of the model is improved by 5%, and the average accuracy is improved by 11%, which also ensures the real-time performance of the algorithm identification. It has a good detection effect on small electrical fittings such as screws.